During April’s Percona Live MySQL Conference and Expo 2014, I attended a talk on MySQL 5.7 performance an scalability given by Dimitri Kravtchuk, the Oracle MySQL benchmark specialist. He mentioned at some point that the InnoDB double write buffer was a real performance killer. For the ones that don’t know what the innodb double write buffer is, it is a disk buffer were pages are written before being written to the actual data file. Upon restart, pages in the double write buffer are rewritten to their data files if complete. This is to avoid data file corruption with half written pages. I knew it has an impact on performance, on ZFS since it is transactional I always disable it, but I never realized how important the performance impact could be. Back from PLMCE, a friend had dropped home a Dell R320 server, asking …

Github user Adrianlzt provided a python-twisted alternative version of pyclustercheck per discussion on issue 7.

Due to sporadic performance issues noted with the original implementation in SimpleHTTPserver, the benchmarks which I’ve included as part of the project on github use mutli-mechanize library,

• cache time 1 sec
• 2 x 100 thread pools
• 60s ramp up time
• 600s total duration
• testing simulated node fail (always returns 503, rechecks mysql node on cache expiry)
• AMD FX(tm)-8350 Eight-Core Processor
• Intel 330 SSD
• local loop back test (127.0.0.1)

The SimpleHTTPServer instance faired as follows:

…

We just released, as an open source release, our new percona-agent (https://github.com/percona/percona-agent), the agent to work with Percona Cloud Tools. This agent is written in Go.

I will give a webinar titled “Monitoring All MySQL Metrics with Percona Cloud Tools” on June 25 that will cover the new features in percona-agent and Percona Cloud Tools, where I will also explain how it works. You are welcome to register now and join me. …

ScaleArc recently hired Percona to perform various tests on its database traffic management product. This post is the outcome of the benchmarks carried out by me and ScaleArc co-founder and chief architect, Uday Sawant.

The goal of this benchmark was to identify ScaleArc’s overhead using a real-world application – the world’s most popular (according to wikipedia) content management system and blog engine: WordPress.

The tests also sought to identify the benefit of caching for this type of workload. The caching parameters represent more real-life circumstances than we applied in the sysbench performance tests – the goal here was not just to saturate the cache. For this reason, we created an artificial WordPress blog with generated data. …

ScaleArc recently hired Percona to perform various tests on its database traffic management product. This post is the outcome of the benchmarks carried out by Uday Sawant (ScaleArc) and myself. You can also download the report directly as a PDF here.

The goal of these benchmarks is to identify the potential overhead of the ScaleArc software itself and the potential benefits of caching. The benchmarks were carried out with the trunk version of sysbench. For this reason, we used a very small set of data, so the measurements will be fast, and it’s known that caching has huge benefits when the queries themselves are rather expensive. We decided that we would rather show that benefit with a real-world application, which is coming later is this series. And if you’re in the Silicon Valley area, be sure to join us this evening at the …

I’ll speak about MySQL 5.7 Performance & Benchmarks during the incoming Percona Live and will be happy to share with you all our latest finding, improvements, benchmark results, open issues and many other stuff keeping our brains in constant activity ) As you know, there is no a “silver bullet” solution for MySQL Performance tuning.. – only by a good understanding of what is going inside of MySQL and InnoDB you may configure your MySQL server in the most optimal way for your workloads. And this topic is endless, and progressing every month with new solutions and features – all parties are working hard here, and I’m very curious to hear from users during this Conference what are their biggest performance issues for today, and of course looking forward to discuss proposed changes from Percona, Facebook, …

In my previous post I pointed out that the existing ARCHIVE storage engine in MySQL may not be the one that will satisfy your needs when it comes to effectively storing large and/or old data. But are there any good alternatives? As the primary purpose of this engine is to store rarely accessed data in disk space efficient way, I will focus here on data compression abilities rather then on performance.

The InnoDB engine provides compressed row format, but is it’s efficiency even close to the one from that available in archive engine? You can also compress MyISAM tables by using myisampack tool, but that also means a table will be read only after such operation.

Moreover, I don’t trust MyISAM nor Archive when it comes to data durability. Fortunately along came a quite new (open source since April …

We just published results with improvements in Thread Pool in Percona Server:
Percona Server: Thread Pool Improvements for Transactional Workloads
Percona Server: Improve Scalability with Thread Pool

What I am happy to see is that Percona Server is able to handle a tremendous amount of user connections. From our charts you can see it can go to 16000 active connections without a decline in throughput.

In fact, in our experiments we used 25000 connections and even more, so I think now we face OS limitations in handling connections as …

In a previous thread pool post, I mentioned that in Percona Server we used an open source implementation of MariaDB’s thread pool, and enhanced/improved it further. Below I would like to describe some of these improvements for transactional workloads.

When we were evaluating MariaDB’s thread pool implementation, we observed that it improves scalability for AUTOCOMMIT statements. However, it does not scale well with multi-statement transactions. The UPDATE_NO_KEY test which was run as an AUTOCOMMIT statement and inside a transaction gave the following results:

After analysis, we identified the major cause of that inefficiency: …

By default, for every client connection the MySQL server spawns a separate thread which will process all statements for this connection. This is the ‘one-thread-per-connection’ model. It’s simple and efficient until some number of connections N is reached. After this point performance of the MySQL server will degrade, mostly due to various contentions caused by N threads that are trying to access shared resources: either system ones like CPU, IO, memory or MySQL specific: structures/locks/etc. To keep the system stable and avoid degradation in the performance we need to limit the number of active threads, and at the same time we do not want to limit number of the client connections. The ‘Thread Pool’ model helps us to achieve that. It allows mapping N client connections to M number of active threads (actually performing work) while demonstrate a smooth and stable throughput for the MySQL server.

There are several implementations of …